{"id":13395825,"url":"https://github.com/ikostrikov/pytorch-flows","last_synced_at":"2025-04-05T02:08:43.668Z","repository":{"id":45777690,"uuid":"147028592","full_name":"ikostrikov/pytorch-flows","owner":"ikostrikov","description":"PyTorch implementations of algorithms for density estimation","archived":false,"fork":false,"pushed_at":"2021-05-13T19:59:29.000Z","size":54,"stargazers_count":571,"open_issues_count":5,"forks_count":77,"subscribers_count":18,"default_branch":"master","last_synced_at":"2024-07-31T18:15:54.507Z","etag":null,"topics":["deep-learning","density-estimation","neural-networks","probabilities","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ikostrikov.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-09-01T19:58:17.000Z","updated_at":"2024-07-23T02:59:08.000Z","dependencies_parsed_at":"2022-09-02T01:20:31.840Z","dependency_job_id":null,"html_url":"https://github.com/ikostrikov/pytorch-flows","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ikostrikov%2Fpytorch-flows","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ikostrikov%2Fpytorch-flows/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ikostrikov%2Fpytorch-flows/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ikostrikov%2Fpytorch-flows/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ikostrikov","download_url":"https://codeload.github.com/ikostrikov/pytorch-flows/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247276164,"owners_count":20912288,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","density-estimation","neural-networks","probabilities","pytorch"],"created_at":"2024-07-30T18:00:33.219Z","updated_at":"2025-04-05T02:08:43.648Z","avatar_url":"https://github.com/ikostrikov.png","language":"Python","funding_links":[],"categories":["🧑‍💻 Code","Python","🧑‍💻 Repos \u003csmall\u003e(18)\u003c/small\u003e","Paper implementations｜论文实现","Paper implementations"],"sub_categories":["\u003cimg src=\"assets/pytorch.svg\" alt=\"PyTorch\" height=\"20px\"\u003e \u0026nbsp;PyTorch Repos","Other libraries｜其他库:","Other libraries:"],"readme":"# pytorch-flows\n\nA PyTorch implementations of [Masked Autoregressive Flow](https://arxiv.org/abs/1705.07057) and \nsome other invertible transformations from [Glow: Generative Flow with Invertible 1x1 Convolutions](https://arxiv.org/pdf/1807.03039.pdf) and [Density estimation using Real NVP](https://arxiv.org/abs/1605.08803).\n\nFor MAF, I'm getting results similar to ones reported in the paper. GLOW requires some work.\n\n## Run\n\n```bash\npython main.py --dataset POWER\n```\n\nAvailable datasets are POWER, GAS, HEPMASS, MINIBONE and BSDS300. For the moment, I removed MNIST and CIFAR10 because I have plans to add pixel-based models later.\n\n## Datasets\n\nThe datasets are taken from the [original MAF repository](https://github.com/gpapamak/maf#how-to-get-the-datasets). Follow the [instructions](https://github.com/gpapamak/maf#how-to-get-the-datasets) to get them.\n\n## Tests\n\nTests check invertibility, you can run them as\n\n```bash\npytest flow_test.py\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fikostrikov%2Fpytorch-flows","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fikostrikov%2Fpytorch-flows","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fikostrikov%2Fpytorch-flows/lists"}